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To detect cerebral aneurysms, arterial stenosis, and other vascular anomalies in a brain CT angiography, we propose a novel technique of cerebral vessel visualization by patient motion correction. Our method has the following steps. First, a set of feature points within the skull base is selected using a 3D edge detection technique. Second, a locally weighted 3D distance map is constructed for leading our similarity measure to robust convergence on the maximum value. Third, the similarity measure between feature points is evaluated repeatedly by selective cross-correlation (SCC). Fourth, the 3D bone-vessel masking and subtraction is performed for completely removing bones. Our method has been successfully applied to five different patients datasets with intracranial aneurysms obtained from 16-slice multi-detector row CT scanner. The total processing time of each datasets was less than 20 seconds. The performance of our method was evaluated with the aspects of accuracy and robustness. For accuracy assessment, we showed results of visual inspection in two-dimensional and three-dimensional comparison of a conventional method and the proposed method. While the quality of the conventional method was substantially reduced by patient motion artifacts, our method could keep the quality of the original image. In particular, intracranial aneurysms were well visualized by our method. Experimental results show that our method is clinically promising by the fact that it is very little influenced by image degradation occurred in bone-vessel interface. For all experimental datasets, we can clearly see intracranial aneurysms as well as arteries on the volumetric images.
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